{"id":"W2154537621","doi":"10.1016/j.media.2009.10.002","title":"CPOL: Complex phase order likelihood as a similarity measure for MR–CT registration","year":2009,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Similarity measure; Similarity (geometry); Artificial intelligence; Measure (data warehouse); Image registration; Pattern recognition (psychology); Fiducial marker; Computer science; Computer vision; Mutual information; Noise (video); Phase (matter); Mathematics; Image (mathematics); Data mining; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00191536,0.0006860681,0.0007408441,0.002801337,0.0005266103,0.00272541,0.001458548,0.001508129,0.004323325],"category_scores_gemma":[0.008218566,0.0004296063,0.0006699114,0.002050017,0.00108263,0.002609353,0.002298299,0.001726826,0.002022238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006435224,"about_ca_system_score_gemma":0.001047372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000819408,"about_ca_topic_score_gemma":0.0008628112,"domain_scores_codex":[0.9988083,0.0003649233,0.00007144507,0.0001544204,0.000532101,0.00006885104],"domain_scores_gemma":[0.9979328,0.0008243519,0.0002937196,0.0003771248,0.0004112895,0.0001607539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007489881,0.0002510088,0.002624637,0.0005357179,0.0001373989,0.0003483929,0.0002867052,0.07125085,0.04675348,0.1595425,0.01558352,0.7019369],"study_design_scores_gemma":[0.00006855508,0.0002258908,0.001908807,0.0000441237,0.00004629172,0.0008903989,0.00008583871,0.8765571,0.03000005,0.07299721,0.01708157,0.00009410229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004458806,0.0001839123,0.99334,0.0001406815,0.00004615258,0.00004347343,0.0001214691,0.0009767399,0.0006886864],"genre_scores_gemma":[0.27298,0.0005288696,0.7168792,0.0003326001,0.0002803579,0.0003660066,0.001083193,0.001884935,0.005664727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004323325,"threshold_uncertainty_score":0.01446301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02893558010175032,"score_gpt":0.3702821879122302,"score_spread":0.3413466078104799,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}